Big Data Challenges: Data Management, Analytics & Security

Size: px
Start display at page:

Download "Big Data Challenges: Data Management, Analytics & Security"

Transcription

1 Big Data Challenges: Data Management, Analytics & Security Ivo D. Dinov Statistics Online Computational Resource University of Michigan

2 Big Data Challenges Availability, Sharing, Aggregation and Services Classical Data Science vs. Innovative Big Data Science Amateur Scientists vs. Experts Data Scientists vs. Practitioners Domain-specific vs. Trans-disciplinary knowledge Commercial vs. Open-source Resourceome Rapid Big Data Evolution Big Data IT proliferation Big Data Security risks Centralization won t work in Big Data Space Big Data is incredibly time, space, protocol, context dependent!

3 Big Data Characteristics * Mixture of quantitative & qualitative estimates Dinov et al. (2013)

4 Availability, Sharing, Aggregation & Services Cisco: "By the end of 2012, the number of mobile-connected devices [exceeded] the number of people on Earth There will be over 10 billion mobile-connected devices in 2016; i.e., there will be 1.3 mobile devices per capita Industry Sector Computer & Electronic Products Information Services Manufacturing Admin, support & waste management Transportation & Warehousing Wholesale Trade Professional Services Healthcare Providers Real Estate and Rental Finance and Insurance Utilities Retail Trade Government Accomodation & Food Arts & Enterntainment Corporate Management Other Services Construction Education Services Natural Resources Percent Growth Bubble Size ~ Relative size of GDP Big Data Value Potential Index U.S. Bureau of Labor Statistics McKinsey Global Institute

5 Amateur Scientists vs. Experts Democratization of Big Data Science Doctorate studies/certification is not mandatory nor does it guarantee appropriate Big Data expertise Lower barriers of entry Demand for constant Continuing Education and self-training Dichotomy between theoretical and empirical sciences Differences between fundamental knowledge and experimental skills (big data properties closely approximate core scientific principles)

6 Domain-specific vs. Trans-disciplinary knowledge Math/Stats Physics Biology Chemistry... Big Data Science Medical Sciences Social Sciences Environmental Sciences... Engineering Computer Science Bioinformatics Biomath/Biostats...

7 Commercial vs. Open-source Resourceome There is an explosion of open-data-science resources Spawning of a number of industries and enterprises blending proprietary and open-source data, code, documentation, expert-support, infrastructure and services Big Data to Knowledge: Google Cloud Platform (GCP) Amazon Web Services (AWS)

8 Commercial vs. Open-source Resourceome

9 Rapid Big Data Evolution Millions of Grass-Roots initiatives addressing Big Data Challenges Big Data complexities require truly innovative, collaborative, trans-disciplinary solutions Increase of Data complexity Sources Heterogeneity Datum-elements Incongruent sampling

10 Data Scientists vs. Practitioners Modelers, Engineers, (Applied) Users No one user completely understands the entire pipeline of data provenance, processing protocols, analytic strategies, or results interpretation Black-boxes. Accuracy Privacy concerns Consistency Infrastructure

11 Big Data Security Risks Big Data Fusion provides enormous opportunities and presents significant challenges Privacy, security and legal concerns, authenticity, accuracy, consistency, reliability, availability Healthcare The cloud services enable sharing big data Significant security and privacy concerns exist, Health Insurance Portability and Accountability Act (HIPAA) EMR/EHR Federal, state and local regulations/policies (IRBMED) Genetics Viral - Dual-use research of concern (DURC), /science de novo synthesis of polio virus, the Australian mousepox experiment, the Penn State aerosolization study

12 Kryder s law: Exponential Growth of Data Increase of Imaging Resolution 6E+15 4E+15 2E µm 10 µm 100 µm 1mm Gryo_Byte Cryo_Short Cryo_Color Cryo_Color Cryo_Short Gryo_Byte 1cm Neuroimaging(GB) Genomics_BP(GB) Moore s Law (1000'sTrans/CPU) Data volume Increases faster than computational power (estimated) Moore s Law (1000'sTrans/CPU) Genomics_BP(GB) Neuroimaging(GB) Dinov, et al., 2013

13 Alzheimer s Case Study: Stable-MCI vs. MCI-Converters Goals predictive-power of combinations of biomarkers and imaging derivative measures to provide reliable predictors of conversion from MCI to Alzheimer s disease Data MCI converters to AD (24-month period) and stable non-converters; matched for age, gender, handedness, education level Imaging (smri), Behavioral, Clinical, Neuropsychiatric, Biological data Approach Qualitative Exploratory Data Analysis and Quantitative Statistical Analysis (morphometric imaging correlates with clinical and genetics markers) MCI = Mild Cognitive Impairment (prelude to dementia of Alzheimer s type)

14

15 Alzheimer s Case Study: Stable-MCI vs. MCI-Converters Subject Demographics Gene -tics Clinical Neuroimaging Index Age Kg Sex APOE A1 APOE A2 NPI SCORE MMSE GD TOTAL CDR FAQ TOTAL L Gyrus Rectus BL L Superior Occipital Gyrus BL R Fusiform Gyrus BL L Caudate BL R Caudate BL L Putamen BL R Putamen BL F M N F

16 Alzheimer s Case Study: Stable-MCI vs. MCI-Converters Classification Results Using Baseline Data Hierarchical Clustering Prediction Ana (7 Regions) Metric True State (Dx at 24 month follow up) Converter Stable Total Converter TP FP TP+FP Stable FN TN FN+TN Total TP+FN FP+TN N Top 7 Regions Value Top 20 Regions Sensitivity Specificity Power to detect Converters Accuracy

Bijan Raahemi, Ph.D., P.Eng, SMIEEE Associate Professor Telfer School of Management and School of Electrical Engineering and Computer Science

Bijan Raahemi, Ph.D., P.Eng, SMIEEE Associate Professor Telfer School of Management and School of Electrical Engineering and Computer Science Bijan Raahemi, Ph.D., P.Eng, SMIEEE Associate Professor Telfer School of Management and School of Electrical Engineering and Computer Science University of Ottawa April 30, 2014 1 Data Mining Data Mining

More information

Participating in Alzheimer s Disease Clinical Trials and Studies

Participating in Alzheimer s Disease Clinical Trials and Studies Participating in Alzheimer s Disease Clinical Trials and Studies FACT SHEET When Margaret was diagnosed with earlystage Alzheimer s disease at age 68, she wanted to do everything possible to combat the

More information

Rates and Predictors of Progression from Mild Cognitive Impairment to Dementia: The Mayo Clinic Study of Aging

Rates and Predictors of Progression from Mild Cognitive Impairment to Dementia: The Mayo Clinic Study of Aging Rates and Predictors of Progression from Mild Cognitive Impairment to Dementia: The Mayo Clinic Study of Aging 11 th Annual Mild Cognitive Impairment Symposium January 19th, 2013 Rosebud Roberts, M.B.

More information

Executive Summary. Principal Findings

Executive Summary. Principal Findings On May 30, 2012, Governor Deval Patrick launched the Massachusetts Big Data Initiative, to leverage and expand the Commonwealth s position as a global leader in the rapidly growing big data sector. The

More information

Economic Overview Monterey County, California. October 2, 2015

Economic Overview Monterey County, California. October 2, 2015 Economic Overview Monterey County, October 2, 2015 DEMOGRAPHIC PROFILE... 3 EMPLOYMENT TRENDS... 5 UNEMPLOYMENT RATE... 5 WAGE TRENDS... 6 COST OF LIVING INDEX... 6 INDUSTRY SNAPSHOT... 7 OCCUPATION SNAPSHOT...

More information

Big Data: Opportunities & Challenges, Myths & Truths 資 料 來 源 : 台 大 廖 世 偉 教 授 課 程 資 料

Big Data: Opportunities & Challenges, Myths & Truths 資 料 來 源 : 台 大 廖 世 偉 教 授 課 程 資 料 Big Data: Opportunities & Challenges, Myths & Truths 資 料 來 源 : 台 大 廖 世 偉 教 授 課 程 資 料 美 國 13 歲 學 生 用 Big Data 找 出 霸 淩 熱 點 Puri 架 設 網 站 Bullyvention, 藉 由 分 析 Twitter 上 找 出 提 到 跟 霸 凌 相 關 的 詞, 搭 配 地 理 位 置

More information

Small Business Data Assess Your Competition Define Your Customers

Small Business Data Assess Your Competition Define Your Customers Small Business Data Assess Your Competition Define Your Customers Census Bureau Data Can Answer Many Questions What Is Census Bureau Data? Economic / business data Economic Census County Business Patterns

More information

Big Analytics: A Next Generation Roadmap

Big Analytics: A Next Generation Roadmap Big Analytics: A Next Generation Roadmap Cloud Developers Summit & Expo: October 1, 2014 Neil Fox, CTO: SoftServe, Inc. 2014 SoftServe, Inc. Remember Life Before The Web? 1994 Even Revolutions Take Time

More information

A Labour Economic Profile of New Brunswick

A Labour Economic Profile of New Brunswick A Labour Economic Profile of New Brunswick January 2016 Table of Contents New Brunswick Highlights........................... 2 Current Business Environment....................... 3 GDP Snapshot....................................

More information

Use advanced techniques for summary and visualization of complex data for exploratory analysis and presentation.

Use advanced techniques for summary and visualization of complex data for exploratory analysis and presentation. MS Biostatistics MS Biostatistics Competencies Study Development: Work collaboratively with biomedical or public health researchers and PhD biostatisticians, as necessary, to provide biostatistical expertise

More information

Analytics for Banks and Finance Companies. November 6, 2016

Analytics for Banks and Finance Companies. November 6, 2016 Analytics for Banks and Finance Companies November 6, 2016 Outline About AlgoAnalytics Problems we can solve Our experience Technology Page 2 About AlgoAnalytics Analytics Consultancy Work at the intersection

More information

Dimensionalizing Big Data. WA State vs. peers. Building on strengths CONTENTS. McKinsey & Company 1

Dimensionalizing Big Data. WA State vs. peers. Building on strengths CONTENTS. McKinsey & Company 1 CONTENTS Building on strengths 1 Printed 2/26/2015 12:55 PM Pacific Standard Time WA State vs. peers Last Modified 3/2/2015 10:17 AM Pacific Standard Time Dimensionalizing Big Data Big Data: big and getting

More information

Search and Data Mining: Techniques. Applications Anya Yarygina Boris Novikov

Search and Data Mining: Techniques. Applications Anya Yarygina Boris Novikov Search and Data Mining: Techniques Applications Anya Yarygina Boris Novikov Introduction Data mining applications Data mining system products and research prototypes Additional themes on data mining Social

More information

Analytics in the Cloud. Peter Sirota, GM Elastic MapReduce

Analytics in the Cloud. Peter Sirota, GM Elastic MapReduce Analytics in the Cloud Peter Sirota, GM Elastic MapReduce Data-Driven Decision Making Data is the new raw material for any business on par with capital, people, and labor. What is Big Data? Terabytes of

More information

Personalized Medicine and IT

Personalized Medicine and IT Personalized Medicine and IT Data-driven Medicine in the Age of Genomics www.intel.com/healthcare/bigdata Ketan Paranjape General Manager, Life Sciences Intel Corp. @Portlandketan 1 The Central Dogma of

More information

Cloud Computing and Health Care Facing the Future. Jerry Fahrni, Pharm.D. April 14, 2010

Cloud Computing and Health Care Facing the Future. Jerry Fahrni, Pharm.D. April 14, 2010 Cloud Computing and Health Care Facing the Future Jerry Fahrni, Pharm.D. April 14, 2010 Objectives Describe what cloud computing is and what cloud computing is not Separate fact from fiction when talking

More information

LexisNexis Provider FAQs

LexisNexis Provider FAQs LexisNexis Provider FAQs Get straight answers to your questions about the provider verification request faxes or phone calls. More than 25 percent of health care provider contact information changes each

More information

NC State University Initiatives in Big Data

NC State University Initiatives in Big Data NC State University Initiatives in Big Data Randy K. Avent 23 May 2014 RKA20140523-1 National Interests in Big Data Managing, processing and exploiting massive data sets for better decision making will

More information

Big Data Trends A Basis for Personalized Medicine

Big Data Trends A Basis for Personalized Medicine Big Data Trends A Basis for Personalized Medicine Dr. Hellmuth Broda, Principal Technology Architect emedikation: Verordnung, Support Prozesse & Logistik 5. Juni, 2013, Inselspital Bern Over 150,000 Employees

More information

20th Alzheimer Europe Conference 2 October 2010, Luxembourg Kristiina Karttunen, Finland

20th Alzheimer Europe Conference 2 October 2010, Luxembourg Kristiina Karttunen, Finland 20th Alzheimer Europe Conference 2 October 2010, Luxembourg Kristiina Karttunen, Finland Background neuropsychiatric symptoms (NPS) are common manifestations of Alzheimer`s disease (AD). These symptoms

More information

A LEADER IN BEHAVIORAL ANALYTICS AND PIONEER IN PERSONALITY-BASED SOFTWARE APPLICATIONS

A LEADER IN BEHAVIORAL ANALYTICS AND PIONEER IN PERSONALITY-BASED SOFTWARE APPLICATIONS A LEADER IN BEHAVIORAL ANALYTICS AND PIONEER IN PERSONALITY-BASED SOFTWARE APPLICATIONS The Chemistry of Conversation Updated June 2015 www. mattersight.com Driving Significant Business Value Every time

More information

Metrics that Matter Security Risk Analytics

Metrics that Matter Security Risk Analytics Metrics that Matter Security Risk Analytics Rich Skinner, CISSP Director Security Risk Analytics & Big Data Brinqa rskinner@brinqa.com April 1 st, 2014. Agenda Challenges in Enterprise Security, Risk

More information

Big Data a threat or a chance?

Big Data a threat or a chance? Big Data a threat or a chance? Helwig Hauser University of Bergen, Dept. of Informatics Big Data What is Big Data? well, lots of data, right? we come back to this in a moment. certainly, a buzz-word but

More information

Session 2. The economics of Cloud Computing

Session 2. The economics of Cloud Computing Session 2. The economics of Cloud Computing Cloud computing is the next step in the on-going evolution of Information Technology. From a technical standpoint, very little that currently is done on cloud

More information

1. Introduction to ehealth:

1. Introduction to ehealth: 1. Introduction to ehealth: E-Health is one of the fastest growing areas within the health sector. The scope of e- Health involves application of the knowledge, skills and tools, which enable information

More information

Predictive Big Data Analytics: Imaging-Genetics Fundamentals, Research Challenges, & Opportunities. Outline

Predictive Big Data Analytics: Imaging-Genetics Fundamentals, Research Challenges, & Opportunities. Outline Predictive Big Data Analytics: Imaging-Genetics Fundamentals, Research Challenges, & Opportunities Ivo D. Dinov Statistics Online Computational Resource Michigan Institute for Data Science Health Behavior

More information

The New World of Data. Don Strickland President, Strickland & Associates

The New World of Data. Don Strickland President, Strickland & Associates The New World of Data Don Strickland President, Strickland & Associates THE NEW WORLD OF DATA 1900 1950 2000 Physical Infrastructure Labor Capital Physical Infrastructure Labor Capital Physical Infrastructure

More information

Regulatory Issues in Genetic Testing and Targeted Drug Development

Regulatory Issues in Genetic Testing and Targeted Drug Development Regulatory Issues in Genetic Testing and Targeted Drug Development Janet Woodcock, M.D. Deputy Commissioner for Operations Food and Drug Administration October 12, 2006 Genetic and Genomic Tests are Types

More information

Requirements for Complex Interactive Workflows in Biomedical Research. Jeffrey S. Grethe, BIRN-CC University of California, San Diego

Requirements for Complex Interactive Workflows in Biomedical Research. Jeffrey S. Grethe, BIRN-CC University of California, San Diego Requirements for Complex Interactive Workflows in Biomedical Research Jeffrey S. Grethe, BIRN-CC University of California, San Diego e-science Workflow Services December 3, 2003 Scientific Workflows Laboratory

More information

GET GOING WITH CUTTING EDGE TECHNOLOGY CUTTING EDGE COST EFFECTIVE CUSTOMER-CENTRIC

GET GOING WITH CUTTING EDGE TECHNOLOGY CUTTING EDGE COST EFFECTIVE CUSTOMER-CENTRIC GET GOING WITH CUTTING EDGE TECHNOLOGY CUTTING EDGE COST EFFECTIVE CUSTOMER-CENTRIC CONTENTS 1 2 3 4 5 About TechMileage & Services Technology Solutions & Expertise Business Domain Expertise What Can We

More information

Introduction to Data Mining

Introduction to Data Mining Introduction to Data Mining Jay Urbain Credits: Nazli Goharian & David Grossman @ IIT Outline Introduction Data Pre-processing Data Mining Algorithms Naïve Bayes Decision Tree Neural Network Association

More information

SCALABLE SYSTEMS LIFE SCIENCE & HEALTHCARE PRACTICES

SCALABLE SYSTEMS LIFE SCIENCE & HEALTHCARE PRACTICES SCALABLE SYSTEMS LIFE SCIENCE & HEALTHCARE PRACTICES Improve Your DNA Data, Numbers & Analytics IntelliPayer Scalable Systems IntelliPayer solution is a next generation healthcare payer solution framework

More information

Industry Sector Analysis

Industry Sector Analysis Industry Sector Analysis Growth, Core, and Competitive-Advantage Industries Southeast Michigan Macomb, Monroe, Oakland, St. Clair and Wayne Counties A Regional Profile Prepared by: Michigan Department

More information

The data explosion is transforming science

The data explosion is transforming science Talk Outline The data tsunami and the 4 th paradigm of science The challenges for the long tail of science Where is the cloud being used now? The app marketplace SMEs Analytics as a service. What are the

More information

Statistics for BIG data

Statistics for BIG data Statistics for BIG data Statistics for Big Data: Are Statisticians Ready? Dennis Lin Department of Statistics The Pennsylvania State University John Jordan and Dennis K.J. Lin (ICSA-Bulletine 2014) Before

More information

Tools for Understanding Economic Change in Communities: Economic Base Analysis and Shift-Share Analysis

Tools for Understanding Economic Change in Communities: Economic Base Analysis and Shift-Share Analysis Tools for Understanding Economic Change in Communities: Economic Base Analysis and Shift-Share Analysis Circular 643A Anil Rupasingha and J. Michael Patrick 1 Cooperative Extension Service College of Agricultural,

More information

ONE WORKFLOW. ONE PARTNER. ACTIONABLE INSIGHTS. Sample to Insight

ONE WORKFLOW. ONE PARTNER. ACTIONABLE INSIGHTS. Sample to Insight ONE WORKFLOW. ONE PARTNER. ACTIONABLE INSIGHTS. Sample to Insight (NGS) Nucleic Acid More actionable data and insights in one go. More confidence at one glance. A small step for you, a giant leap for your

More information

Labor Force & Economic Profile Metropolitan Denver Highlighting Arapahoe & Douglas Counties

Labor Force & Economic Profile Metropolitan Denver Highlighting Arapahoe & Douglas Counties C O L O R A D O Labor Force & Economic Profile Metropolitan Denver 2016 Highlighting Arapahoe & Douglas Counties Labor Force & Economic Profile Metropolitan Denver 2016 1 2 3 4 5 6-7 8 9 10-11 12-13 14

More information

LDIF - Linked Data Integration Framework

LDIF - Linked Data Integration Framework LDIF - Linked Data Integration Framework Andreas Schultz 1, Andrea Matteini 2, Robert Isele 1, Christian Bizer 1, and Christian Becker 2 1. Web-based Systems Group, Freie Universität Berlin, Germany a.schultz@fu-berlin.de,

More information

Red Flags. Sx > 6 weeks. Trauma. Unexplained wt loss. age > 50 with comp fx or hx of osteoporosis. Age > 70. Fever, infection

Red Flags. Sx > 6 weeks. Trauma. Unexplained wt loss. age > 50 with comp fx or hx of osteoporosis. Age > 70. Fever, infection Acute Low Back Pain Most common cause of disability < 45 y Cost: Billions (US) annually in dx, rx, lost work time Acute uncomplicated (no red flags) is self limited - No imaging required. Need for focused

More information

Big Data Analytics Empowering SME s to think and act

Big Data Analytics Empowering SME s to think and act Big Data Analytics Empowering SME s to think and act Author: Haricharan Mylaraiah Chief Operating Officer 1320 Greenway Drive, Irving, TX 75038 Contents Executive Summary... 3 Introduction... 4 What Big

More information

Collaborations between Official Statistics and Academia in the Era of Big Data

Collaborations between Official Statistics and Academia in the Era of Big Data Collaborations between Official Statistics and Academia in the Era of Big Data World Statistics Day October 20-21, 2015 Budapest Vijay Nair University of Michigan Past-President of ISI vnn@umich.edu What

More information

ENTERPRISE MOBILE APPLICATIONS: ENABLING EMPLOYEES - ENGAGING CUSTOMERS. Background

ENTERPRISE MOBILE APPLICATIONS: ENABLING EMPLOYEES - ENGAGING CUSTOMERS. Background Background In recent years mobile devices have become a truly viable platform for application delivery. The original App stores have driven user community awareness and familiarity and with the number

More information

Elevate your analytics with SAS in the cloud

Elevate your analytics with SAS in the cloud Elevate your analytics with SAS in the cloud Cloud$56 BILLION The Cloud SAS & Cloud Cloud in New Zealand The Cloud CHARACTERISTICS SERVICE MODELS DEPLOYMENT MODELS On-Demand Self Service Broad Network

More information

Data Mining and Machine Learning in Bioinformatics

Data Mining and Machine Learning in Bioinformatics Data Mining and Machine Learning in Bioinformatics PRINCIPAL METHODS AND SUCCESSFUL APPLICATIONS Ruben Armañanzas http://mason.gmu.edu/~rarmanan Adapted from Iñaki Inza slides http://www.sc.ehu.es/isg

More information

Business Case Development for Credit and Debit Card Fraud Re- Scoring Models

Business Case Development for Credit and Debit Card Fraud Re- Scoring Models Business Case Development for Credit and Debit Card Fraud Re- Scoring Models Kurt Gutzmann Managing Director & Chief ScienAst GCX Advanced Analy.cs LLC www.gcxanalyacs.com October 20, 2011 www.gcxanalyacs.com

More information

CLASSIFYING NETWORK TRAFFIC IN THE BIG DATA ERA

CLASSIFYING NETWORK TRAFFIC IN THE BIG DATA ERA CLASSIFYING NETWORK TRAFFIC IN THE BIG DATA ERA Professor Yang Xiang Network Security and Computing Laboratory (NSCLab) School of Information Technology Deakin University, Melbourne, Australia http://anss.org.au/nsclab

More information

Big Data in Healthcare. Dr. Refael Barkan, M.D. Ph.D. Head of RDE at HIT Bar-Ilan University, May 2016

Big Data in Healthcare. Dr. Refael Barkan, M.D. Ph.D. Head of RDE at HIT Bar-Ilan University, May 2016 Big Data in Healthcare Dr. Refael Barkan, M.D. Ph.D. Head of RDE at HIT Bar-Ilan University, May 2016 What is Big Data? Quite nebulous, in the same way that the term cloud covers diverse technologies To

More information

PPD LABORATORIES CENTRAL LAB: SUPERIOR SERVICE, QUALITY DATA WITHOUT COMPROMISES

PPD LABORATORIES CENTRAL LAB: SUPERIOR SERVICE, QUALITY DATA WITHOUT COMPROMISES PPD LABORATORIES CENTRAL LAB: SUPERIOR SERVICE, QUALITY DATA WITHOUT COMPROMISES PPD Laboratories provides world-class scientific expertise with state-of-the-art technologies supported by a commitment

More information

Capgemini Big Data Analytics Sandbox for Financial Services

Capgemini Big Data Analytics Sandbox for Financial Services Capgemini Big Data Analytics Sandbox for Financial Services Put your data to use quickly without spending a fortune 2 Capgemini Big Data Analytics Sandbox for Financial Services Table of Contents 1. A

More information

Prediction of the MoCA and the MMSE in Out-patients with the risks of cognitive impairment

Prediction of the MoCA and the MMSE in Out-patients with the risks of cognitive impairment Prediction of the MoCA and the MMSE in Out-patients with the risks of cognitive impairment Teresa Leung Therapist Prince of Wales Hospital 7 th May, 2012 Outline of Presentation Introduction Study Objectives,

More information

ANXIETY & COGNITIVE IMPAIRMENT

ANXIETY & COGNITIVE IMPAIRMENT ANXIETY & COGNITIVE IMPAIRMENT Dr. Sherri Hayden, Ph.D., R. Psych. Neuropsychologist, UBC Hospital Clinic for Alzheimer Disease & Related Disorders Clinical Assistant Professor, UBC Department of Medicine,

More information

Healthcare data analytics. Da-Wei Wang Institute of Information Science wdw@iis.sinica.edu.tw

Healthcare data analytics. Da-Wei Wang Institute of Information Science wdw@iis.sinica.edu.tw Healthcare data analytics Da-Wei Wang Institute of Information Science wdw@iis.sinica.edu.tw Outline Data Science Enabling technologies Grand goals Issues Google flu trend Privacy Conclusion Analytics

More information

Research trends relevant to data warehousing and OLAP include [Cuzzocrea et al.]: Combining the benefits of RDBMS and NoSQL database systems

Research trends relevant to data warehousing and OLAP include [Cuzzocrea et al.]: Combining the benefits of RDBMS and NoSQL database systems DATA WAREHOUSING RESEARCH TRENDS Research trends relevant to data warehousing and OLAP include [Cuzzocrea et al.]: Data source heterogeneity and incongruence Filtering out uncorrelated data Strongly unstructured

More information

Factors for success in big data science

Factors for success in big data science Factors for success in big data science Damjan Vukcevic Data Science Murdoch Childrens Research Institute 16 October 2014 Big Data Reading Group (Department of Mathematics & Statistics, University of Melbourne)

More information

Open-source analytics in the Enterprise-level environment. Opportunities and challenges. Maciej Zawadziński CEO of Piwik PRO

Open-source analytics in the Enterprise-level environment. Opportunities and challenges. Maciej Zawadziński CEO of Piwik PRO Open-source analytics in the Enterprise-level environment. Opportunities and challenges. Maciej Zawadziński CEO of Piwik PRO ABOUT Maciej Zawadziński Technical leader and entrepreneur since 2003 CEO of

More information

Frequently Asked Questions

Frequently Asked Questions Frequently Asked Questions Business Office: 598 Airport Boulevard Suite 1400 Morrisville NC 27560 Contact: support@cognitrax.com Phone: 888.750.6941 Fax: 888.650.6795 www.cognitrax.com Diseases of the

More information

The History of NAICS

The History of NAICS The History of NAICS By James T. Saint, CCIM Real Estate Advocate 5 Apr 2007 While many real estate professionals and business executives are reasonably familiar with the older Standard Industrial Classification

More information

Master of Science in Computer Science. Option Health Information Systems

Master of Science in Computer Science. Option Health Information Systems Master of Science in Computer Science Option Health Information Systems 1. The program Currently, in the Lebanese and most of Middle East s hospitals, the management of health information systems is handled

More information

BioVisualization: Enhancing Clinical Data Mining

BioVisualization: Enhancing Clinical Data Mining BioVisualization: Enhancing Clinical Data Mining Even as many clinicians struggle to give up their pen and paper charts and spreadsheets, some innovators are already shifting health care information technology

More information

Michigan Economic Development Corporation

Michigan Economic Development Corporation Michigan Economic Development Corporation 300 N. Washington Square, Lower Level Lansing, Michigan 48913 888.522.0103 Economy Overview MEDC Region 10 Southeast Michigan Economic Modeling Specialists International

More information

SECURITY RISK MANAGEMENT

SECURITY RISK MANAGEMENT SECURITY RISK MANAGEMENT ISACA Atlanta Chapter, Geek Week August 20, 2013 Scott Ritchie, Manager, HA&W Information Assurance Services Scott Ritchie CISSP, CISA, PCI QSA, ISO 27001 Auditor Manager, HA&W

More information

Cognitive Testing for Underwriting Life Insurance

Cognitive Testing for Underwriting Life Insurance Cognitive Testing for Underwriting Life Insurance Presentation to the Mortality Working Group of the International Actuarial Association Al Klein April 8, 2011 Cognitive function Agenda What is it? What

More information

On-Time, On-Target Clinical Documentation Meets Today s Demands on Your Terms

On-Time, On-Target Clinical Documentation Meets Today s Demands on Your Terms On-Time, On-Target Clinical Documentation Meets Today s Demands on Your Terms High-Quality, Cost-Effective, Timely Clinical Documentation: Meeting Today s Demands on Your Terms The Challenge The ever-expanding

More information

Veriday solves your greatest engagement challenges by building industry leading online experiences.

Veriday solves your greatest engagement challenges by building industry leading online experiences. Veriday solves your greatest engagement challenges by building industry leading online experiences. STRATEGY DESIGN TECHNOLOGY TRUSTED DIGITAL INNOVATORS Veriday is a Technology and Digital Marketing Firm

More information

Big Data, Official Statistics and Social Science Research: Emerging Data Challenges

Big Data, Official Statistics and Social Science Research: Emerging Data Challenges Big Data, Official Statistics and Social Science Research: Emerging Data Challenges Professor Paul Cheung Director, United Nations Statistics Division Building the Global Information System Elements of

More information

CLUSTER ANALYSIS WITH R

CLUSTER ANALYSIS WITH R CLUSTER ANALYSIS WITH R [cluster analysis divides data into groups that are meaningful, useful, or both] LEARNING STAGE ADVANCED DURATION 3 DAY WHAT IS CLUSTER ANALYSIS? Cluster Analysis or Clustering

More information

Big Data Applications in Health. Suleyman Akbas

Big Data Applications in Health. Suleyman Akbas Big Data Applications in Health Suleyman Akbas Overview 1. Papers 2. Introduction 3. Big Data in Health 4. Potential Use Areas 5. Current Applications 6. Privacy Issues 7. Discussion Papers 1. Big Data

More information

2015 Public Cloud Disaster Recovery Survey

2015 Public Cloud Disaster Recovery Survey 2015 Public Cloud Disaster Recovery Survey Disaster Recovery Challenges and Best Practices Executive Summary This benchmark survey presents challenges and best practices of companies that host web applications

More information

Big Data Analytics in Health Care

Big Data Analytics in Health Care Big Data Analytics in Health Care S. G. Nandhini 1, V. Lavanya 2, K.Vasantha Kokilam 3 1 13mss032, 2 13mss025, III. M.Sc (software systems), SRI KRISHNA ARTS AND SCIENCE COLLEGE, 3 Assistant Professor,

More information

Application of SAS! Enterprise Miner in Credit Risk Analytics. Presented by Minakshi Srivastava, VP, Bank of America

Application of SAS! Enterprise Miner in Credit Risk Analytics. Presented by Minakshi Srivastava, VP, Bank of America Application of SAS! Enterprise Miner in Credit Risk Analytics Presented by Minakshi Srivastava, VP, Bank of America 1 Table of Contents Credit Risk Analytics Overview Journey from DATA to DECISIONS Exploratory

More information

Confused, Dependent and Challenging

Confused, Dependent and Challenging Confused, Dependent and Challenging The Prevalence of Mental Health Problems Amongst Older Adults Admitted to a General Hospital Sarah Goldberg sarah.goldberg@nottingham.ac.uk Disgraceful care leaves dementia

More information

ORACLE HEALTH SCIENCES INFORM ADVANCED MOLECULAR ANALYTICS

ORACLE HEALTH SCIENCES INFORM ADVANCED MOLECULAR ANALYTICS ORACLE HEALTH SCIENCES INFORM ADVANCED MOLECULAR ANALYTICS INCORPORATE GENOMIC DATA INTO CLINICAL R&D KEY BENEFITS Enable more targeted, biomarker-driven clinical trials Improves efficiencies, compressing

More information

Primary Endpoints in Alzheimer s Dementia

Primary Endpoints in Alzheimer s Dementia Primary Endpoints in Alzheimer s Dementia Dr. Karl Broich Federal Institute for Drugs and Medical Devices (BfArM) Kurt-Georg-Kiesinger-Allee 38, D-53175 Bonn Germany Critique on Regulatory Decisions in

More information

Validation parameters: An introduction to measures of

Validation parameters: An introduction to measures of Validation parameters: An introduction to measures of test accuracy Types of tests All tests are fundamentally quantitative Sometimes we use the quantitative result directly However, it is often necessary

More information

Turning SIC to NAICS, where do we stand?

Turning SIC to NAICS, where do we stand? Turning SIC to NAICS, where do we stand? Frederick Treyz, CEO Regional Economic Models, Inc. Federation of Tax Administrators Conference September 23, 2003 Overview of the North American Industry Classification

More information

Wages of Employed Texans Who Attended Texas Public Schools

Wages of Employed Texans Who Attended Texas Public Schools Wage Comparision by Educational Attainment for Texans Age 25 to 30 Median 4th Quarter Wages Number Employed Earnings Year 2010 2011 2012 2010 2011 2012 Educational Attainment Advanced Bachelor's Associate

More information

On-Time, On-Target Clinical Documentation Meets Today s Demands on Your Terms

On-Time, On-Target Clinical Documentation Meets Today s Demands on Your Terms On-Time, On-Target Clinical Documentation Meets Today s Demands on Your Terms High-Quality, Cost-Effective, Timely Clinical Documentation: Meeting Today s Demands on Your Terms The Challenge The ever-expanding

More information

Big Data Executive Survey

Big Data Executive Survey Big Data Executive Full Questionnaire Big Date Executive Full Questionnaire Appendix B Questionnaire Welcome The survey has been designed to provide a benchmark for enterprises seeking to understand the

More information

TRENDS IN DATA WAREHOUSING

TRENDS IN DATA WAREHOUSING TRENDS IN DATA WAREHOUSING Chapter #3 Imran Khan Agenda Continued Growth in DW DW has become Mainstream Industries using DW Vendor Solution & Products Status of DW market Significant Trends Web Enabled

More information

The Economic Impact of Fire Damage on Wyoming s Economy from a Business Perspective

The Economic Impact of Fire Damage on Wyoming s Economy from a Business Perspective The Economic Impact of Fire Damage on Wyoming s Economy from a Business Perspective A Regional Economic Models, Inc. (REMI) Policy Insight Analysis Prepared by Amy Bittner and Justin Ballard August 18,

More information

Why is BIG Data Important?

Why is BIG Data Important? Why is BIG Data Important? March 2012 1 Why is BIG Data Important? A Navint Partners White Paper May 2012 Why is BIG Data Important? March 2012 2 What is Big Data? Big data is a term that refers to data

More information

Utilizing big data to bring about innovative offerings and new revenue streams DATA-DERIVED GROWTH

Utilizing big data to bring about innovative offerings and new revenue streams DATA-DERIVED GROWTH Utilizing big data to bring about innovative offerings and new revenue streams DATA-DERIVED GROWTH ACTIONABLE INTELLIGENCE Ericsson is driving the development of actionable intelligence within all aspects

More information

Clinical Research Infrastructure

Clinical Research Infrastructure Clinical Research Infrastructure Enhancing UK s Clinical Research Capabilities & Technologies At least 150m to establish /develop cutting-edge technological infrastructure, UK wide. to bring into practice

More information

Science-Specific Search: Bridging the Gap between Dissemination & Access to Information

Science-Specific Search: Bridging the Gap between Dissemination & Access to Information Science-Specific Search: Bridging the Gap between Dissemination & Access to Information Presented by: Joris van Rossum, Head of Scirus Event: IATUL 2007 Content How has content provision changed? How has

More information

An EVIDENCE-ENHANCED HEALTHCARE ECOSYSTEM for Cancer: I/T perspectives

An EVIDENCE-ENHANCED HEALTHCARE ECOSYSTEM for Cancer: I/T perspectives An EVIDENCE-ENHANCED HEALTHCARE ECOSYSTEM for Cancer: I/T perspectives Chalapathy Neti, Ph.D. Associate Director, Healthcare Transformation, Shahram Ebadollahi, Ph.D. Research Staff Memeber IBM Research,

More information

EVERYTHING THAT MATTERS IN ADVANCED ANALYTICS

EVERYTHING THAT MATTERS IN ADVANCED ANALYTICS EVERYTHING THAT MATTERS IN ADVANCED ANALYTICS Marcia Kaufman, Principal Analyst, Hurwitz & Associates Dan Kirsch, Senior Analyst, Hurwitz & Associates Steve Stover, Sr. Director, Product Management, Predixion

More information

IMPLEMENTING BIG DATA IN TODAY S HEALTH CARE PRAXIS: A CONUNDRUM TO PATIENTS, CAREGIVERS AND OTHER STAKEHOLDERS - WHAT IS THE VALUE AND WHO PAYS

IMPLEMENTING BIG DATA IN TODAY S HEALTH CARE PRAXIS: A CONUNDRUM TO PATIENTS, CAREGIVERS AND OTHER STAKEHOLDERS - WHAT IS THE VALUE AND WHO PAYS IMPLEMENTING BIG DATA IN TODAY S HEALTH CARE PRAXIS: A CONUNDRUM TO PATIENTS, CAREGIVERS AND OTHER STAKEHOLDERS - WHAT IS THE VALUE AND WHO PAYS 29 OCTOBER 2015 DR. DIRK J. EVERS BACKGROUND TreatmentMAP

More information

Segmentation: Foundation of Marketing Strategy

Segmentation: Foundation of Marketing Strategy Gelb Consulting Group, Inc. 1011 Highway 6 South P + 281.759.3600 Suite 120 F + 281.759.3607 Houston, Texas 77077 www.gelbconsulting.com An Endeavor Management Company Overview One purpose of marketing

More information

MEDICAL DATA MINING. Timothy Hays, PhD. Health IT Strategy Executive Dynamics Research Corporation (DRC) December 13, 2012

MEDICAL DATA MINING. Timothy Hays, PhD. Health IT Strategy Executive Dynamics Research Corporation (DRC) December 13, 2012 MEDICAL DATA MINING Timothy Hays, PhD Health IT Strategy Executive Dynamics Research Corporation (DRC) December 13, 2012 2 Healthcare in America Is a VERY Large Domain with Enormous Opportunities for Data

More information

EVALUATION OF AUTOMATIC CLASS III DESIGNATION FOR STUDIO on the Cloud Data Management Software DECISION SUMMARY

EVALUATION OF AUTOMATIC CLASS III DESIGNATION FOR STUDIO on the Cloud Data Management Software DECISION SUMMARY A. DEN Number: DEN140016 EVALUATION OF AUTOMATIC CLASS III DESIGNATION FOR STUDIO on the Cloud Data Management Software B. Purpose for Submission: DECISION SUMMARY De novo request for adjunct data management

More information

Predicting Medication Compliance and Persistency

Predicting Medication Compliance and Persistency Predicting Medication Compliance and Persistency By: Jay Bigelow, President Amanda Rhodes, M.P.H., C.H.E.S., Vice President Behavioral Solutions MicroMass Communications, Inc. Introduction A widely recognized

More information

The Future of Broadband Internet Access in Canada

The Future of Broadband Internet Access in Canada The Future of Broadband Internet Access in Canada CRTC Telecom Notice of Consultation 2013-551 Introduction Cybera is a not- for- profit, technology- neutral agency responsible for accelerating high- tech

More information

Developing Data Analytics Skills in Japan: Status and Challenge

Developing Data Analytics Skills in Japan: Status and Challenge Developing Data Analytics Skills in Japan: Status and Challenge Hiroshi Maruyama, The Institute of Statistical Mathematics Abstract: Japan needs to develop data analytics talents quickly to catch up with

More information

ADNI Data Training Part 2

ADNI Data Training Part 2 ADNI Data Training Part 2 ADNI Biostatistics Core Team UC Davis School of Medicine Department of Public Health Sciences August 1, 2013 Outline Data Overview Today s Presentation Outline Data overview Commonly

More information

Discover more, discover faster. High performance, flexible NLP-based text mining for life sciences

Discover more, discover faster. High performance, flexible NLP-based text mining for life sciences Discover more, discover faster. High performance, flexible NLP-based text mining for life sciences It s not information overload, it s filter failure. Clay Shirky Life Sciences organizations face the challenge

More information

Trends. Big Data...The next frontier

Trends. Big Data...The next frontier IT industry Trends Big Data...The next frontier Space: the final frontier. To paraphrase Star Trek, Big Data may help your business boldly go where no business has gone before. And while it may not be

More information

Proposal for the Theme on Big Data. Analytics. Qiang Yang, HKUST Jiannong Cao, PolyU Qi-man Shao, CUHK. May 2015

Proposal for the Theme on Big Data. Analytics. Qiang Yang, HKUST Jiannong Cao, PolyU Qi-man Shao, CUHK. May 2015 Proposal for the Theme on Big Data Analytics May 2015 Qiang Yang, HKUST Jiannong Cao, PolyU Qi-man Shao, CUHK Motivation The world's technological per-capita capacity to store information doubled every

More information

Big Data Visualization for Genomics. Luca Vezzadini Kairos3D

Big Data Visualization for Genomics. Luca Vezzadini Kairos3D Big Data Visualization for Genomics Luca Vezzadini Kairos3D Why GenomeCruzer? The amount of data for DNA sequencing is growing Modern hardware produces billions of values per sample Scientists need to

More information

Dementia: Delivering the Diagnosis

Dementia: Delivering the Diagnosis Dementia: Delivering the Diagnosis Daniel D. Christensen, M.D. Clinical Professor of Psychiatry Clinical Professor of Neurology Adjunct Professor of Pharmacology University of Utah Diagnosing Dementia

More information